Overview of Manifold Learning Techniques for the Investigation of Disruptions on JET

Identifying a low dimensional embedding of a high dimensional data set allows exploring the data structure. In this paper we tested some existing manifold learning techniques for discovering such embedding within the multidimensional operational space of a nuclear fusion tokamak. Among the manifold learning methods the following approaches have been investigated: linear methods, as Principal Component Analysis, and Grand Tour, and nonlinear methods as Self Organizing Map and its probabilistic variant, the Generative Topographic Mapping. In particular, the last two methods allows us to obtain a low-dimensional (typically 2-D) map of the high dimensional operational space of the tokamak. These maps provide a way to visualize the structure of the high dimensional plasma parameters space and allow to discriminate between regions characterized by high risk of disruption and low risk of disruption. The data for this study comes from plasma discharges selected from 2005 and up to 2009 at JET. SOM and GTM provide the most benefits in visualization of very large and high-dimensional datasets. Some measures have been used to evaluate their performance. Special emphasis has been put on the position of outliers and extreme points, quantization errors and topological errors.
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